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40 years ago Harvard undergrad Bill Gates published an algorithm for sorting pancakes. His co-author's first thought when he learned Bill had dropped out to start Microsoft: "Such a brilliant kid. What a waste." More about it, here: n.pr/PanGates
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Have you ever used the KNN algorithm in an interesting application? Let us know in the comments section, right below!
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TOP TRENDS in Technology for the next decade! Do you agree? Let us know in the comments section, right below!
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The BIG DATA landscape of 2019! Check the entire infographic here: http://mattturck.com/wp-content/uploads/2019/06/2019_Matt_Turck_Big_Data_Landscape_2019_Fullsize.png
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Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own algorithm, or logic based on the data.
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For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic. ✔
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This machine learning algorithm is a black box that can be re-used for lots of different classification problems.
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“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms. Credits: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.qxf7bryp4
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WERBUNG what do you lose most on 30 degree summer days? Yes, it's water and minerals. Have you tried the new Aquarius drink? ☺️ It restores us after a stressful day. One bottle of Aquarius lemon, for example, ☝ is giving us 30% of our daily intake of Zinc with minimal amount of calories Great when you feel like a loser and need a pickup aka sitting with the same bug in our code for hours —> take a walk, a bottle of Aquarius and then target the bug again #werbung #goagain #aquarius @aquarius_deutschland
Nope, this isn’t creepy at all... Meet the Nvidianator, a 3D-printed Terminator skull with object and facial recognition. Powered by NVIDIA's Jetson Nano, the desktop robot is capable of keeping track of conversations as well as learning new faces and logging names.
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// bit.ly/nvidianator
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Natural Language Processing (NLP) is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, an increasingly data mining. The ultimate of NLP is to read, decipher, understand, and make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text.
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While NLP can be traced back to the 1950s, when computer programmers began experimenting with simple language input, Natural Language Understanding (NLU) began developing in the 1960s out of a desire to get computers to understand more complex language input. Considered a subtopic of NLP, natural language is narrower in purpose, focusing primarily on machine reading comprehension: getting the computer to comprehend what a text really means. NLU uses algorithms to reduce human speech into a structured ontology. Then AI algorithms detect such things as intent, timing, locations and sentiments. Credits: https://medium.com/sciforce/nlp-vs-nlu-from-understanding-a-language-to-its-processing-1bf1f62453c1
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